Search

Surface solar radiation (SSR) is an essential factor in the flow of surface energy, enabling accurate capturing of long-term climate change and understanding of the energy balance of Earth's atmosphere system. However, the long-term trend estimation of SSR is subject to significant uncertainties due to the temporal inhomogeneity and the uneven spatial distribution of in situ observations. This paper develops an observational integrated and homogenized global terrestrial (except for Antarctica) station SSR dataset (SSRIH station) by integrating all available SSR observations, including the existing homogenized SSR results. The series is then interpolated in order to obtain a 5 ∘ × 5 ∘ resolution gridded dataset (SSRIH grid). On this basis, we further reconstruct a long-term (1955–2018) global land (except for Antarctica) SSR anomaly dataset with a 5 ∘ × 2.5 ∘ resolution (SSRIH 20CR) by training improved partial convolutional neural network deep-learning methods based on 20th Century Reanalysis version 3 (20CRv3). Based on this, we analysed the global land- (except for Antarctica) and regional-scale SSR trends and spatiotemporal variations. The reconstruction results reflect the distribution of SSR anomalies and have high reliability in filling and reconstructing the missing values. At the global land (except for Antarctica) scale, the decreasing trend of the SSRIH 20CR (- 1.276 ± 0.205 W m -2 per decade) is smaller than the trend of the SSRIH grid (- 1.776 ± 0.230 W m -2 per decade) from 1955 to 1991. The trend of the SSRIH 20CR (0.697 ± 0.359 W m -2 per decade) from 1991 to 2018 is also marginally lower than that of the SSRIH grid (0.851 ± 0.410 W m -2 per decade). At the regional scale, the difference between the SSRIH 20CR and SSRIH grid is more significant in years and areas with insufficient coverage. Asia, Africa, Europe and North America cause the global dimming of the SSRIH 20CR , while Europe and North America drive the global brightening of the SSRIH 20CR. Spatial sampling inadequacies have largely contributed to a bias in the long-term variation of global and regional SSR. This paper's homogenized gridded dataset and the Artificial Intelligence reconstruction gridded dataset (Jiao and Li, 2023) are both available at 10.6084/m9.figshare.21625079.v1. [ABSTRACT FROM AUTHOR]

The NASA Atmospheric Tomography Mission (ATom) completed four seasonal deployments (August 2016, February 2017, October 2017, May 2018), each with regular 0.2–12 km profiling by transecting the remote Pacific Ocean and Atlantic Ocean basins. Additional data were also acquired for the Southern Ocean, the Arctic basin, and two flights over Antarctica. ATom in situ measurements provide a near-complete chemical characterization of the ∼ 140 000 10 s (80 m by 2 km) air parcels measured along the flight path. This paper presents the Modeling Data Stream (MDS), a continuous gap-filled record of the 10 s parcels containing the chemical species needed to initialize a gas-phase chemistry model for the budgets of tropospheric ozone and methane. Global 3D models have been used to calculate the Reactivity Data Stream (RDS), which is comprised of the chemical reactivities (production and loss) for methane, ozone, and carbon monoxide, through 24 h integration of the 10 s parcels. These parcels accurately sample tropospheric heterogeneity and allow us to partially deconstruct the spatial scales and variability that define tropospheric chemistry from composition to reactions. This paper provides a first look at and analysis of the up-to-date MDS and RDS data including all four deployments (Prather et al., 2023, 10.7280/D1B12H). ATom's regular profiling of the ocean basins allows for weighted averages to build probability densities for the key species and reactivities presented here. These statistics provide climatological metrics for global chemistry models, e.g., the large-scale pattern of ozone and methane loss in the lower troposphere and the more sporadic hotspots of ozone production in the upper troposphere. The profiling curtains of reactivity also identify meteorologically variable and hence deployment-specific hotspots of photochemical activity. Added calculations of the sensitivities of the production and loss terms relative to each species emphasize the few dominant species that control the ozone and methane budgets and whose statistical patterns should be key model–measurement metrics. From the sensitivities, we also derive linearized lifetimes of ozone and methane on a parcel-by-parcel basis and average over the basins, providing an observational basis for these previously model-only diagnostics. We had found that most model differences in the ozone and methane budgets are caused by the models calculating different climatologies for the key species such as O 3 , CO, H 2 O, NO x , CH 4 , and T , and thus these ATom measurements make a substantial contribution to the understanding of model differences and even identifying model errors in global tropospheric chemistry. [ABSTRACT FROM AUTHOR]

Surface solar radiation (SSR) is an essential factor in the flow of surface energy, enabling accurate capturing of long-term climate change and understanding the energy balance of Earth's atmosphere system. However, the long-term trend estimation of SSR is subjected to significant uncertainties due to the temporal inhomogeneity and the uneven spatial distribution of the in-situ observations. This paper develops an observational integrated and homogenized global-terrestrial (except for Antarctica)) stational SSR dataset (SSRIHstation) by integrating all available SSR observations, including the existing homogenized SSR results. The series are then interpolated in order to obtain a 5°×5° resolution gridded dataset (SSRIHgrid). On this basis, we further reconstruct a long-term (1955-2018) global land (except for Antarctica) SSR anomalies dataset with a 5°×2.5° resolution (SSRIH20CR) by training improved partial convolutional neural network deep learning methods based on the reanalysis 20CRv3. Based on this, we analysed the global land (except for Antarctica)/regional scale SSR trends and spatiotemporal variations: the reconstruction results reflect the distribution of SSR anomalies and have high reliability in filling and reconstructing the missing values. At the global land (except for Antarctica) scale, the decreasing trend of the SSRIH20CR (-1.276±0.205 W/m2 per decade) is slightly smaller than the trend of the SSRIHgrid (- 1.776±0.230 W/m2 per decade) from 1955 to 1991. The trend of SSRIH20CR (0.697±0.359 W/m2 per decade) from 1991 to 2018 is also marginally lower than that of the SSRIHgrid (0.851±0.410 W/m2 per decade). At the regional scale, the difference between the SSRIH20CR and SSRIHgrid is more significant in years and areas with insufficient coverage. Asia, Africa, Europe and North America cause the global dimming of the SSRIH20CR, while Europe and North America drive the global brightening of the SSRIH20CR. Spatial sampling inadequacies have largely contributed to a bias in the long-term variation of global/regional SSR. This paper's homogenized gridded dataset and the Artificial Intelligence reconstruction gridded dataset (Jiao and Li, 2023) are all available at. [ABSTRACT FROM AUTHOR]

The NASA Atmospheric Tomography (ATom) Mission completed four seasonal deployments (August 2016, February 2017, October 2017, May 2018), each with regular 0.2-12 km profiling through transecting the remote Pacific and Atlantic Ocean basins. Additional data are acquired also for the Southern Ocean and Artic basin, as well as two flights over Antarctica. ATom in situ measurements provide a near-complete chemical characterization of the ~140,000 10-second (80 m by 2 km) air parcels measured along the flight path. This paper presents the Modeling Data Stream (MDS), a continuous gap-filled record of the 10-s parcels containing the chemical species needed to initialize a gas-phase chemistry model for the budgets of tropospheric ozone and methane. Global 3D models have been used to calculate the Reactivity Data Stream RDS), which is comprised of the chemical reactivities (production and loss) for methane, ozone, and carbon monoxide, through 24-hour integration of the 10-s parcels. These parcels accurately sample tropospheric heterogeneity and allow us to partially deconstruct the spatial scales and variability that defines tropospheric chemistry from composition to reactions. This paper provides a first look and analysis of the up-to-date MDS and RDS data including all four deployments (Prather et al., 2023, doi: 10.7280/D1B12H). ATom's regular profiling of the ocean basins allows for weighted averages to build probability densities for key species and reactivities presented here. These statistics provide climatological metrics for global chemistry models, for example, the large-scale pattern of ozone and methane loss in the lower troposphere, and the more sporadic hot spots of ozone production in the upper troposphere. The profiling curtains of reactivity also identify meteorologically variable and hence deploymentspecific hot spots of photochemical activity. Added calculations of the sensitivities of the production and loss terms relative to each species emphasize the few dominant species that control the ozone and methane budgets, and whose statistical patterns should be key model-measurement metrics. From the sensitivities, we also derive linearized lifetimes of ozone and methane on a parcel-by-parcel basis and average over the basins, providing an observational basis for these previously model-only diagnostics. We had found that most model differences in the ozone and methane budgets are caused by the models calculating different climatologies for the key species such as O3, CO, H2O, NOx, CH4 plus T, and thus these ATom measurements provide a substantial contribution to the understanding of model differences and even identifying model errors in global tropospheric chemistry. [ABSTRACT FROM AUTHOR]

As the climate warms, the grounded ice sheet and floating ice shelves surrounding Antarctica are melting and releasing additional freshwater into the Southern Ocean. Nonetheless, almost all existing coupled climate models have fixed ice sheets and lack the physics required to represent the dominant sources of Antarctic melt. These missing ice dynamics represent a key uncertainty that is typically unaccounted for in current global climate change projections. Previous modelling studies that have imposed additional Antarctic meltwater have demonstrated regional impacts on Southern Ocean stratification, circulation, and sea ice, as well as remote changes in atmospheric circulation, tropical precipitation, and global temperature. However, these previous studies have used widely varying rates of freshwater forcing, have been conducted using different climate models and configurations, and have reached differing conclusions on the magnitude of meltwater–climate feedbacks. The Southern Ocean Freshwater Input from Antarctica (SOFIA) initiative brings together a team of scientists to quantify the climate system response to Antarctic meltwater input along with key aspects of the uncertainty. In this paper, we summarize the state of knowledge on meltwater discharge from the Antarctic ice sheet and ice shelves to the Southern Ocean and explain the scientific objectives of our initiative. We propose a series of coupled and ocean–sea ice model experiments, including idealized meltwater experiments, historical experiments with observationally consistent meltwater input, and future scenarios driven by meltwater inputs derived from stand-alone ice sheet models. Through coordinating a multi-model ensemble of simulations using a common experimental design, open data archiving, and facilitating scientific collaboration, SOFIA aims to move the community toward better constraining our understanding of the climate system response to Antarctic melt. [ABSTRACT FROM AUTHOR]

This paper introduces a unique multiyear dataset and the monitoring capability of the PANDA automatic weather station network, which includes 11 automatic weather stations (AWSs) across the Prydz Bay–Amery Ice Shelf–Dome A area from the coast to the summit of the East Antarctic Ice Sheet. The ∼ 1460 km transect from Zhongshan to Panda S follows roughly along ∼ 77 ∘ E longitude and covers all geographic units of East Antarctica. Initial inland observations, near the coast, started in the 1996/97 austral summer. All AWSs in this network measure air temperature, relative humidity, air pressure, wind speed and wind direction at 1 h intervals, and some of them can also measure firn temperature and shortwave/longwave radiation. Data are relayed in near real time via the Argos system. The data quality is generally very reliable, and the data have been used widely. In this paper, we firstly present a detailed overview of the AWSs, including the sensor characteristics, installation procedure, data quality control protocol and the basic analysis of each variable. We then give an example of a short-term atmospheric event that shows the monitoring capacity of the PANDA AWS network. This dataset, which is publicly available, is planned to be updated on a near-real-time basis and should be valuable for climate change estimation, extreme weather events diagnosis, data assimilation, weather forecasting, etc. The dataset is available at 10.11888/Atmos.tpdc.272721 (Ding et al., 2022b). [ABSTRACT FROM AUTHOR]

Observations of ocean-driven grounding-line retreat in the Amundsen Sea Embayment in Antarctica raise the question of an imminent collapse of the West Antarctic Ice Sheet. Here we analyse the committed evolution of Antarctic grounding lines under the present-day climate. To this aim, we first calibrate a sub-shelf melt parameterization, which is derived from an ocean box model, with observed and modelled melt sensitivities to ocean temperature changes, making it suitable for present-day simulations and future sea level projections. Using the new calibration, we run an ensemble of historical simulations from 1850 to 2015 with a state-of-the-art ice sheet model to create model instances of possible present-day ice sheet configurations. Then, we extend the simulations for another 10 000 years to investigate their evolution under constant present-day climate forcing and bathymetry. We test for reversibility of grounding-line movement in the case that large-scale retreat occurs. In the Amundsen Sea Embayment we find irreversible retreat of the Thwaites Glacier for all our parameter combinations and irreversible retreat of the Pine Island Glacier for some admissible parameter combinations. Importantly, an irreversible collapse in the Amundsen Sea Embayment sector is initiated at the earliest between 300 and 500 years in our simulations and is not inevitable yet – as also shown in our companion paper Part 1,. In other words, the region has not tipped yet. With the assumption of constant present-day climate, the collapse evolves on millennial timescales, with a maximum rate of 0.9 mma-1 sea-level-equivalent ice volume loss. The contribution to sea level by 2300 is limited to 8 cm with a maximum rate of 0.4 mma-1 sea-level-equivalent ice volume loss. Furthermore, when allowing ice shelves to regrow to their present geometry, we find that large-scale grounding-line retreat into marine basins upstream of the Filchner–Ronne Ice Shelf and the western Siple Coast is reversible. Other grounding lines remain close to their current positions in all configurations under present-day climate. [ABSTRACT FROM AUTHOR]

Theoretical and numerical work has shown that under certain circumstances grounding lines of marine-type ice sheets can enter phases of irreversible advance and retreat driven by the marine ice sheet instability (MISI). Instances of such irreversible retreat have been found in several simulations of the Antarctic Ice Sheet. However, it has not been assessed whether the Antarctic grounding lines are already undergoing MISI in their current position. Here, we conduct a systematic numerical stability analysis using three state-of-the-art ice sheet models: Úa, Elmer/Ice, and the Parallel Ice Sheet Model (PISM). For the first two models, we construct steady-state initial configurations whereby the simulated grounding lines remain at the observed present-day positions through time. The third model, PISM, uses a spin-up procedure and historical forcing such that its transient state is close to the observed one. To assess the stability of these simulated states, we apply short-term perturbations to submarine melting. Our results show that the grounding lines around Antarctica migrate slightly away from their initial position while the perturbation is applied, and they revert once the perturbation is removed. This indicates that present-day retreat of Antarctic grounding lines is not yet irreversible or self-sustained. However, our accompanying paper (Part 2,) shows that if the grounding lines retreated further inland, under present-day climate forcing, it may lead to the eventual irreversible collapse of some marine regions of West Antarctica. [ABSTRACT FROM AUTHOR]

Solid precipitation measurements in Antarctica are crucial as snowfall represents the main water input term for the polar cap, and its probable increase in the coming century can mitigate sea-level rise caused by global warming. This paper presents 7 years of Micro Rain Radar (Metek MRR-2) data at the Dumont d'Urville station in coastal Adélie Land, East Antarctica. Statistics are calculated on 3 radar variables (equivalent reflectivity, mean Doppler velocity and signal-to-noise ratio) to outline the main characteristics of the radar dataset. Seasonal and interannual variabilities are also investigated, but no significant temporal trends are detected except for the seasonal mean Doppler velocity which is higher in summer and lower in winter. We then use the snowfall rate (S) data from a colocated snow-gauge to estimate the MRR precipitation profile from the radar equivalent reflectivity (Ze) through a locally derived Ze-S relation. We find the relation Ze = 43.3 S0.88. The processing method used to obtain this relation, data quality and uncertainty considerations are discussed in the manuscript. In order to give an example of application of the dataset, a brief statistical comparison of the MRR precipitation rate along the vertical with model data from the ERA5 reanalysis and the LMDZ climate model is performed, and notably shows that models underestimate heavy precipitation events. [ABSTRACT FROM AUTHOR]

Large-ensemble modelling has become an increasingly popular approach to studying the mean climate and the climate system's internal variability in response to external forcing. Here we present the Royal Netherlands Meteorological Institute (KNMI) Large Ensemble Time Slice (KNMI–LENTIS): a new large ensemble produced with the re-tuned version of the global climate model EC-Earth3. The ensemble consists of two distinct time slices of 10 years each: a present-day time slice and a +2 K warmer future time slice relative to the present day. The initial conditions for the ensemble members are generated with a combination of micro- and macro-perturbations. The 10-year length of a single time slice is assumed to be too short to show a significant forced climate change signal, and the ensemble size of 1600 years (160 × 10 years) is assumed to be sufficient to sample the full distribution of climate variability. The time slice approach makes it possible to study extreme events on sub-daily timescales as well as events that span multiple years such as multi-year droughts and preconditioned compound events. KNMI–LENTIS is therefore uniquely suited to study internal variability and extreme events both at a given climate state and resulting from forced changes due to external radiative forcing. A unique feature of this ensemble is the high temporal output frequency of the surface water balance and surface energy balance variables, which are stored in 3-hourly intervals, allowing for detailed studies into extreme events. The large ensemble is particularly geared towards research in the land–atmosphere domain. EC-Earth3 has a considerable warm bias in the Southern Ocean and over Antarctica. Hence, users of KNMI–LENTIS are advised to make in-depth comparisons with observational or reanalysis data, especially if their studies focus on ocean processes, on locations in the Southern Hemisphere, or on teleconnections involving both hemispheres. In this paper, we will give some examples to demonstrate the added value of KNMI–LENTIS for extreme- and compound-event research and for climate-impact modelling. [ABSTRACT FROM AUTHOR]

In studying the mass balance of polar ice sheets, fluctuations in firn density near the surface is a major uncertainty. In this paper, we explore these variations at locations on the Greenland Ice Sheet and at the Dome C location in Antarctica. Borehole in situ measurements, snow radar echoes, microwave brightness temperatures, and modeling results from the Community Firn Model (CFM) are used. It is shown that firn density profiles can be represented using three processes: "long-scale" and "short-scale" density variations and "refrozen layers". Consistency with this description is observed in the dynamic range of airborne 0.5–2 GHz brightness temperatures and snow radar echo peaks in measurements performed in Greenland in 2017. Based on these insights, a new analytical partially coherent model is implemented to explain the microwave brightness temperatures using the three-scale description of the firn. Short- and long-scale firn processes are modeled as a 3D continuous random medium with finite vertical and horizontal correlation lengths as opposed to past 1D randomly layered medium descriptions. Refrozen layers are described as deterministic sheets with planar interfaces, with the number of refrozen-layer interfaces determined by radar observations. Firn density and correlation length parameters used in forward modeling to match measured 0.5–2 GHz brightness temperatures in Greenland show consistency with similar parameters in CFM predictions. Model predictions also are in good agreement with multi-angle 1.4 GHz vertically and horizontally polarized brightness temperature measured by the Soil Moisture and Ocean Salinity (SMOS) satellite at Dome C, Antarctica. This work shows that co-located active and passive microwave measurements can be used to infer polar firn properties that can be compared with predictions of the CFM. In particular, 0.5–2 GHz brightness temperature measurements are shown to be sensitive to long-scale firn density fluctuations with density standard deviations in the range of 0.01–0.06 gcm-3 and vertical correlation lengths of 6–20 cm. [ABSTRACT FROM AUTHOR]

Antarctic coastal polynyas produce dense shelf water, a primary source of Antarctic Bottom Water that contributes to the global overturning circulation. This paper investigates Antarctic dense water formation in the high-resolution version of the Energy Exascale Earth System Model (E3SM-HR). The model is able to reproduce the main Antarctic coastal polynyas, although the polynyas are smaller in area compared to observations. E3SM-HR also simulates several occurrences of open-ocean polynyas (OOPs) in the Weddell Sea at a higher rate than what the last 50 years of the satellite sea ice observational record suggests, but similarly to other high-resolution Earth system model simulations. Furthermore, the densest water masses in the model are formed within the OOPs rather than on the continental shelf as is typically observed. Biases related to the lack of dense water formation on the continental shelf are associated with overly strong atmospheric polar easterlies, which lead to a strong Antarctic Slope Front and too little exchange between on- and off-continental shelf water masses. Strong polar easterlies also produce excessive southward Ekman transport, causing a build-up of sea ice over the continental shelf and enhanced ice melting in the summer season. This, in turn, produces water masses on the continental shelf that are overly fresh and less dense relative to observations. Our results indicate that high resolution alone is insufficient for models to properly reproduce Antarctic dense water; the large-scale polar atmospheric circulation around Antarctica must also be accurately simulated. [ABSTRACT FROM AUTHOR]

This paper introduces a unique multiyear dataset and the monitoring capability of the PANDA automatic weather station network which includes eleven automatic weather stations (AWS) across Prydz Bay-Amery Ice Shelf-dome area from the coast to the summit of the East Antarctica ice sheet. The ~1460 km transect from Zhongshan to Panda S station follows roughly along ~77° E longitude and covers all geographic and climatic units of East Antarctica. Initial inland observation, near the coast, started in the 1996/1997 austral summer. All AWSs in this network measure air temperature, relative humidity, air pressure, wind speed and wind direction at 1-hour intervals, and some of them can also measure firn temperature and shortwave/longwave radiation. Data are relayed in near real-time via the ARGOS system. Data quality is generally very reliable and the data have been used widely. In this paper, we firstly present a detailed overview of the AWSs, including the sensor characteristics, installation procedure, data quality control protocol, and the basic analysis of each variable. We then give an example of a short-term atmospheric event that shows the monitoring capacity of the network. This dataset, which is publicly available, is planned to be updated on a near-real time and should be valuable for climate change estimation, extreme weather events diagnosis, data assimilation, weather forecasting, etc. The dataset is available at https://doi.org/10.11888/Atmos.tpdc.272721 (Ding et al., 2022). [ABSTRACT FROM AUTHOR]

Large-ensemble modelling has become an increasingly popular approach to study the mean climate and the climate system's internal variability in response to external forcing. Here we present the KNMI Large ENsemble TIme Slice (KNMILENTIS): a new large ensemble data set produced with the re-turned version of the global climate model EC-Earth3. The ensemble consists of two distinct time slices of 10 year each: a present-day time slice and a +2K warmer future time slice relative to present-day. The initial conditions for the ensemble members are generated with a combination of micro and macro perturbations. The 10-year length of a single time slice is assumed to be too short to show a significant forced climate change signal, and the ensemble size of 1600 years (160×10 years) is assumed to be sufficient to sample the full distribution of climate variability. The time-slice approach makes it possible to study extreme events on sub-annual timescales as well as events that span multiple years such as multi-year droughts and preconditioned compound events. KNMI-LENTIS is therefore uniquely suited to study internal variability and extreme events both at a given climate state and those resulting from forced changes due to external radiative forcing. A unique feature of this ensemble is the high temporal output frequency of the surface water balance and surface energy balance variables, which are stored in 3-hourly intervals, allowing for detailed studies into extreme events. The data set is particularly geared towards research in the land-atmosphere domain. EC-Earth3 has a considerable warm bias in the Southern Ocean and over Antarctica. Hence, users of KNMI-LENTIS are advised to make in-depth comparisons with observational or reanalysis data especially if their studies focus on ocean processes, on locations in the Southern Hemisphere or on teleconnections involving both hemispheres. In this paper, we will give some examples to demonstrate the added value of KNMI-LENTIS for extreme and compound event research and for climate-impact modelling. [ABSTRACT FROM AUTHOR]

Studying precipitation at very high latitudes is difficult because of the harsh environmental conditions that limit the external activity of humans and instruments, especially in the polar winter. The direct monitoring of ice crystal habits and size distribution in Antarctic precipitation is important for the validation of the algorithms used for retrieving precipitation from ground-based and satellite-borne radar instruments and for the improvement of the climatological modelling of polar areas. This paper describes an automated device (ICE-CAMERA) specifically developed for the imaging, measurement, and classification of ice precipitation on the Antarctic high plateau. The instrument gives detailed information on precipitation on an hourly basis. The article provides a description of the device and its image processing software. Starting in 2014, the instrument has operated almost unattended all year round at Concordia station, Antarctica (75 ∘ S, 123 ∘ E, 3220 m altitude). [ABSTRACT FROM AUTHOR]

Evidence for the timing and pace of past grounding line retreat of the Thwaites Glacier system in the Amundsen Sea embayment (ASE) of Antarctica provides constraints for models that are used to predict the future trajectory of the West Antarctic Ice Sheet (WAIS). Existing cosmogenic nuclide surface exposure ages suggest that Pope Glacier, a former tributary of Thwaites Glacier, experienced rapid thinning in the early to mid-Holocene. There are relatively few exposure ages from the lower ice-free sections of Mt. Murphy (<300 m a.s.l.; metres above sea level) that are uncomplicated by either nuclide inheritance or scatter due to localised topographic complexities; this makes the trajectory for the latter stages of deglaciation uncertain. This paper presents 12 new 10 Be exposure ages from erratic cobbles collected from the western flank of Mt. Murphy, within 160 m of the modern ice surface and 1 km from the present grounding line. The ages comprise two tightly clustered populations with mean deglaciation ages of 7.1 ± 0.1 and 6.4 ± 0.1 ka (1 SE). Linear regression analysis applied to the age–elevation array of all available exposure ages from Mt. Murphy indicates that the median rate of thinning of Pope Glacier was 0.27 m yr -1 between 8.1–6.3 ka, occurring 1.5 times faster than previously thought. Furthermore, this analysis better constrains the uncertainty (95 % confidence interval) in the timing of deglaciation at the base of the Mt. Murphy vertical profile (∼ 80 m above the modern ice surface), shifting it to earlier in the Holocene (from 5.2 ± 0.7 to 6.3 ± 0.4 ka). Taken together, the results presented here suggest that early- to mid-Holocene thinning of Pope Glacier occurred over a shorter interval than previously assumed and permit a longer duration over which subsequent late Holocene re-thickening could have occurred. [ABSTRACT FROM AUTHOR]

Continental-scale land cover information is essential to furthering our understanding of the terrestrial environment, atmosphere and climate change. Several global land cover products have been released in recent years but they typically do not include Antarctica. The lack of land cover data in Antarctica is concerning because mountain glaciers and icecaps there have been losing mass at a rate well above the global average, leading to expansion of proglacial regions. Proglacial regions comprise transient land cover types with high rates of geomorphological activity that delivers sediment into the Southern Ocean and supports its rich biodiversity. With Antarctic mountain glaciers and icecaps projected to lose more mass in the coming decades, and active layer soils expected to increase in thickness, it is timely to establish a baseline land cover dataset for Antarctica with which future classifications can be compared. Here, we use Landsat-8 Operational Land Imager (OLI) images to classify six proglacial regions of Antarctica at 30 m resolution, with an overall accuracy of 77.0 % for proglacial land classes. We conducted this classification using an unsupervised K-means clustering approach, which circumvented the need for training data and was highly effective at picking up key land classes, such as vegetation, water, and different sedimentary surfaces. We have highlighted the spatial pattern in land cover and emphasise a need for more and higher quality field data. The land cover maps produced from this paper are available at: Stringer, C. (2022). Contemporary (2016 - 2020) land cover classification across West Antarctica and the McMurdo Dry Valleys (Version 1.0) [Data set]. NERC EDS UK Polar Data Centre. [ABSTRACT FROM AUTHOR]

Exact information on the calving front positions of marine- or lake-terminating glaciers is a fundamental glacier variable for analyzing ongoing glacier change processes and assessing other variables like frontal ablation rates. In recent years, researchers started implementing algorithms that can automatically detect the calving fronts on satellite imagery. Most studies use optical images, as calving fronts are often easy to distinguish in these images due to the sufficient spatial resolution and the presence of different spectral bands, allowing the separation of ice features. However, detecting calving fronts on synthetic aperture radar (SAR) images is highly desirable, as SAR images can also be acquired during the polar night and are independent of weather conditions (e.g., cloud cover), facilitating year-round monitoring worldwide. In this paper, we present a benchmark dataset of SAR images from multiple regions of the globe with corresponding manually defined labels providing information on the position of the calving front (https://doi.org/10.1594/PANGAEA.940950). With this dataset, different approaches for the detection of glacier calving fronts can be implemented, tested, and their performance fairly compared so that the most effective approach can be determined. The dataset consists of 681 samples, making it large enough to train deep learning segmentation models. It is the first dataset to provide long-term glacier calving front information from multi-mission data. As the dataset includes glaciers from Antarctica, Greenland, and Alaska, the wide applicability of models trained and tested on this dataset is ensured. The test set is independent of the training set so that the generalization capabilities of the models can be evaluated. We provide two sets of labels: one binary segmentation label to discern the calving front from the background, and one label for multi-class segmentation of different landscape classes. Unlike other calving front datasets, the presented dataset contains not only the labels but also the corresponding preprocessed and geo-referenced SAR images as PNG files. The ease of access to the dataset will allow scientists from other fields, such as data science, to contribute their expertise. With this benchmark dataset, we enable comparability between different front detection algorithms and improve the reproducibility of front detection studies. Moreover, we present one baseline model for each kind of label type. Both models are based on the U-Net, one of the most popular deep learning segmentation architectures. In the following two post-processing procedures, the segmentation results are converted into 1-pixel-wide front delineations. By providing both types of labels, both approaches can be used to address the problem. To assess the performance of different models, we suggest first reviewing the segmentation results using the recall, precision, F1 score, and the Jaccard index. Second, the front delineation can be evaluated by calculating the mean distance error to the labeled front. The presented vanilla models provide a baseline of 150 m ± 24 m mean distance error for the Mapple Glacier in Antarctica and 840 m ± 84 m for the Columbia Glacier in Alaska, which has a more complex calving front, consisting of multiple sections, compared with a laterally well constrained, single calving front of Mapple Glacier. [ABSTRACT FROM AUTHOR]

Regional climate models (RCMs) and reanalysis datasets provide valuable information for assessing the vulnerability of ice shelves to collapse over Antarctica, which is important for future global sea level rise estimates. Within this context, this paper examines variability in snowfall, near-surface air temperature and melt across products from the Met Office Unified Model (MetUM), Regional Atmospheric Climate Model (RACMO) and Modèle Atmosphérique Régional (MAR) RCMs, as well as the ERA-Interim and ERA5 reanalysis datasets. Seasonal and trend decomposition using LOESS (STL) is applied to split the monthly time series at each model grid cell into trend, seasonal and residual components. Significant systematic differences between outputs are shown for all variables in the mean and in the seasonal and residual standard deviations, occurring at both large and fine spatial scales across Antarctica. Results imply that differences in the atmospheric dynamics, parametrisation, tuning and surface schemes between models together contribute more significantly to large-scale variability than differences in the driving data, resolution, domain specification, ice sheet mask, digital elevation model and boundary conditions. Despite significant systematic differences, high temporal correlations are found for snowfall and near-surface air temperature across all products at fine spatial scales. For melt, only moderate correlation exists at fine spatial scales between different RCMs and low correlation between RCM and reanalysis outputs. Root mean square deviations (RMSDs) between all outputs in the monthly time series for each variable are shown to be significant at fine spatial scales relative to the magnitude of annual deviations. Correcting for systematic differences results in significant reductions in RMSDs, suggesting the importance of observations and further development of bias-correction techniques. [ABSTRACT FROM AUTHOR]

Accurately modelling the contribution of Greenland and Antarctica to sea level rise requires solving partial differential equations at a high spatial resolution. In this paper, we discuss the scaling of the Ice-sheet and Sea-level System Model (ISSM) applied to the Greenland Ice Sheet with horizontal grid resolutions varying between 10 and 0.25 km. The model setup used as benchmark problem comprises a variety of modules with different levels of complexity and computational demands. The core builds the so-called stress balance module, which uses the higher-order approximation (or Blatter–Pattyn) of the Stokes equations, including free surface and ice-front evolution as well as thermodynamics in form of an enthalpy balance, and a mesh of linear prismatic finite elements, to compute the ice flow. We develop a detailed user-oriented, yet low-overhead, performance instrumentation tailored to the requirements of Earth system models and run scaling tests up to 6144 Message Passing Interface (MPI) processes. The results show that the computation of the Greenland model scales overall well up to 3072 MPI processes but is eventually slowed down by matrix assembly, the output handling and lower-dimensional problems that employ lower numbers of unknowns per MPI process. We also discuss improvements of the scaling and identify further improvements needed for climate research. The instrumented version of ISSM thus not only identifies potential performance bottlenecks that were not present at lower core counts but also provides the capability to continually monitor the performance of ISSM code basis. This is of long-term significance as the overall performance of ISSM model depends on the subtle interplay between algorithms, their implementation, underlying libraries, compilers, runtime systems and hardware characteristics, all of which are in a constant state of flux. We believe that future large-scale high-performance computing (HPC) systems will continue to employ the MPI-based programming paradigm on the road to exascale. Our scaling study pertains to a particular modelling setup available within ISSM and does not address accelerator techniques such as the use of vector units or GPUs. However, with 6144 MPI processes, we identified issues that need to be addressed in order to improve the ability of the ISSM code base to take advantage of upcoming systems that will require scaling to even higher numbers of MPI processes. [ABSTRACT FROM AUTHOR]

Over the past 50 years, the British Antarctic Survey (BAS) has been one of the major acquirers of aerogeophysical data over Antarctica, providing scientists with gravity, magnetic, and radar datasets that have been central to many studies of the past, present, and future evolution of the Antarctic Ice Sheet. Until recently, many of these datasets were not openly available, restricting further usage of the data for different glaciological and geophysical applications. Starting in 2020, scientists and data managers at BAS have worked on standardizing and releasing large swaths of aerogeophysical data acquired during the period 1994–2020, including a total of 64 datasets from 24 different surveys, amounting to ∼ 450 000 line-km (or 5.3 million km 2) of data across West Antarctica, East Antarctica, and the Antarctic Peninsula. Amongst these are the extensive surveys over the fast-changing Pine Island (BBAS 2004–2005) and Thwaites (ITGC 2018–2019 & 2019–2020) glacier catchments, and the first ever surveys of the Wilkes Subglacial Basin (WISE-ISODYN 2005–2006) and Gamburtsev Subglacial Mountains (AGAP 2007–2009). Considerable effort has been made to standardize these datasets to comply with the FAIR (findable, accessible, interoperable and re-usable) data principles, as well as to create the Polar Airborne Geophysics Data Portal (https://www.bas.ac.uk/project/nagdp/ , last access: 18 July 2022), which serves as a user-friendly interface to interact with and download the newly published data. This paper reviews how these datasets were acquired and processed, presents the methods used to standardize them, and introduces the new data portal and interactive tutorials that were created to improve the accessibility of the data. Lastly, we exemplify future potential uses of the aerogeophysical datasets by extracting information on the continuity of englacial layering from the fully published airborne radar data. We believe these newly released data will be a valuable asset to future glaciological and geophysical studies over Antarctica and will significantly extend the life cycle of the data. All datasets included in this data release are now fully accessible at https://data.bas.ac.uk (British Antarctic Survey, 2022). [ABSTRACT FROM AUTHOR]

The Antarctic plateau, characterized by cold and dry weather conditions with very little precipitation, is mostly covered by snow at the surface. This paper describes an intercomparison of snow models, of varying complexity, used for numerical weather prediction or academic research. The results of offline numerical simulations, carried out during 15 d in 2009, on a single site on the Antarctic plateau, show that the simplest models are able to reproduce the surface temperature as well as the most complex models provided that their surface parameters are well chosen. Furthermore, it is shown that the diversity of the surface parameters of the models strongly impacts the numerical simulations, in particular the temporal variability of the surface temperature and the components of the surface energy balance. The models tend to overestimate the surface temperature by 2–5 K at night and underestimate it by 2 K during the day. The observed and simulated turbulent latent heat fluxes are small, of the order of a few W m -2 , with a tendency to underestimate, while the sensible heat fluxes are in general too intense at night as well as during the day. The surface temperature errors are consistent with too large a magnitude of sensible heat fluxes during the day and night. Finally, it is shown that the most complex multilayer models are able to reproduce well the propagation of the daily diurnal wave, and that the snow temperature profiles in the snowpack are very close to the measurements carried out on site. [ABSTRACT FROM AUTHOR]

Exact information on calving front positions of marine-or lake-terminating glaciers is a fundamental glacier variable for analyzing ongoing glacier change processes and assessing other variables like frontal ablation rates. In recent years, researchers started implementing algorithms that could automatically detect the calving fronts on satellite imagery. Most studies use optical images, as in these images, calving fronts are often easy to distinguish due to sufficient spatial resolution and the presence of different spectral bands, allowing the separation of ice features. However, detecting calving fronts on SAR images is highly desirable, as SAR images can also be acquired during the polar night and are independent of weather conditions, e. g., cloud cover, facilitating all-year monitoring worldwide. In this paper, we present a benchmark dataset of SAR images from multiple regions of the globe with corresponding manually defined labels to train and test approaches for the detection of glacier calving fronts. The dataset is the first to provide long-term glacier calving front information from multi-mission data. As the dataset includes glaciers from Antarctica, Greenland and Alaska, the wide applicability of models trained and tested on this dataset is ensured. The test set is independent of the training set so that the generalization capabilities of the models can be evaluated. We provide two sets of labels: one binary segmentation label to discern the calving front from the background and one for multi-class segmentation of different landscape classes. Unlike other calving front datasets, the presented dataset contains not only the labels but also the corresponding preprocessed and geo-referenced SAR images as PNG files. The ease of access to the dataset will allow scientists from other fields, such as data science, to contribute their expertise. With this benchmark dataset, we enable comparability between different front detection algorithms and improve the reproducibility of front detection studies. Moreover, we present one baseline model for each kind of label type. Both models are based on the U-Net, one of the most popular deep learning segmentation architectures. Additionally, we introduce Atrous Spatial Pyramid Pooling to the bottleneck layer. In the following two post-processing procedures, the segmentation results are converted into one-pixel-wide front delineations. By providing both types of labels, both approaches can be used to address the problem. To assess the performance of the models, we first review the segmentation results using the recall, precision, F1-score, and the Jaccard Index. Second, we evaluate the front delineation by calculating the mean distance error to the labeled front. The presented vanilla models provide a baseline of 150m±24m mean distance error for the Mapple Glacier in Antarctica and 840m±84m for the Columbia Glacier in Alaska, which has a more complex calving front, consisting of multiple sections, as compared to a laterally well constrained, single calving front of Mapple Glacier. [ABSTRACT FROM AUTHOR]

We explore a methodology to statistically downscale snowfall – the primary driver of surface mass balance in Antarctica – from an ensemble of historical (1850–present day) simulations performed with an earth system model over the coastal region of Dronning Maud Land (East Antarctica). This approach consists of associating daily snowfall simulations from a polar-oriented regional atmospheric climate model at 5.5 km spatial resolution with specific weather patterns observed over 1979–2010 CE with the atmospheric reanalyses ERA-Interim and ERA5. This association is then used to generate the spatial distribution of snowfall for the period from 1850 to present day for an ensemble of 10 members from the Community Earth System Model (CESM2). The new dataset of daily and yearly snowfall accumulation based on this methodology is presented in this paper (MASS2ANT dataset; 10.5281/zenodo.4287517;). Based on a comparison with available ice cores and spatial reconstructions, our results show that the spatio-temporal distribution of snowfall is improved in the downscaled dataset compared with the CESM2 simulations. This dataset thus provides information that may be useful in identifying the large-scale patterns associated with the local precipitation conditions and their changes over the past century. [ABSTRACT FROM AUTHOR]

Monitoring the evolution of ice shelf damage such as crevasses and rifts is important for a better understanding of the mechanisms controlling the breakup of ice shelves and for improving predictions about iceberg calving and ice shelf disintegration. Nowadays, the previously existing observational gap has been reduced by the Copernicus Sentinel-1 synthetic aperture radar (SAR) mission that provides a continuous coverage of the Antarctic margins with a 6 or 12 d repeat period. The unprecedented coverage and temporal sampling enables, for the first time, a year-round systematic monitoring of ice shelf fracturing and iceberg calving, as well as the detection of precursor signs of calving events. In this paper, a novel method based on SAR interferometry is presented for an automatic detection and delineation of active cracks on ice shelves. Propagating cracks cause phase discontinuities that are extracted automatically by applying a Canny edge detection procedure to the spatial phase gradient derived from a SAR interferogram. The potential of the proposed method is demonstrated in the case of Brunt Ice Shelf, Antarctica, using a stack of 6 d repeat-pass Sentinel-1 interferograms acquired between September 2020 and March 2021. The full life cycle of the North Rift is monitored, including the rift detection, its propagation at rates varying between 0.25 and 1.30 km d -1 , and the final calving event that gave birth to the iceberg A74 on 26 February 2021. The automatically delineated cracks agree well with the North Rift location in Landsat 8 images and with the eventual location of the ice shelf edge after the iceberg broke off. The strain variations observed in the interferograms are attributed to a rigid-body rotation of the ice about the expanding tip of the North Rift in response to the rifting activity. The extent of the North Rift is captured by SAR interferometry well before it becomes visible in SAR backscatter images and a few days before it could be identified in optical images, hence highlighting the high sensitivity of SAR interferometry to small variations in the ice shelf strain pattern and its potential for detecting early signs of natural calving events, ice shelf fracturing and damage development. [ABSTRACT FROM AUTHOR]

Assessing the impact of uncertainties in ice-sheet models is a major and challenging issue that needs to be faced by the ice-sheet community to provide more robust and reliable model-based projections of ice-sheet mass balance. In recent years, uncertainty quantification (UQ) has been increasingly used to characterize and explore uncertainty in ice-sheet models and improve the robustness of their projections. A typical UQ analysis first involves the (probabilistic) characterization of the sources of uncertainty, followed by the propagation and sensitivity analysis of these sources of uncertainty. Previous studies concerned with UQ in ice-sheet models have generally focused on the last two steps but have paid relatively little attention to the preliminary and critical step of the characterization of uncertainty. Sources of uncertainty in ice-sheet models, like uncertainties in ice-sheet geometry or surface mass balance, typically vary in space and potentially in time. For that reason, they are more adequately described as spatio-(temporal) random fields, which account naturally for spatial (and temporal) correlation. As a means of improving the characterization of the sources of uncertainties for forward UQ analysis within the Ice-sheet and Sea-level System Model (ISSM), we present in this paper a stochastic sampler for Gaussian random fields with Matérn covariance function. The class of Matérn covariance functions provides a flexible model able to capture statistical dependence between locations with different degrees of spatial correlation or smoothness properties. The implementation of this stochastic sampler is based on a notable explicit link between Gaussian random fields with Matérn covariance function and a certain stochastic partial differential equation. Discretization of this stochastic partial differential equation by the finite-element method results in a sparse, scalable and computationally efficient representation known as a Gaussian Markov random field. In addition, spatio-temporal samples can be generated by combining an autoregressive temporal model and the Matérn field. The implementation is tested on a set of synthetic experiments to verify that it captures the desired spatial and temporal correlations well. Finally, we illustrate the interest of this stochastic sampler for forward UQ analysis in an application concerned with assessing the impact of various sources of uncertainties on the Pine Island Glacier, West Antarctica. We find that larger spatial and temporal correlations lengths will both likely result in increased uncertainty in the projections. [ABSTRACT FROM AUTHOR]

Over the past 50 years, the British Antarctic Survey (BAS) has been one of the major acquirers of aerogeophysical data over Antarctica, providing scientists with gravity, magnetic and radar datasets that have been central to many studies of the past, present, and future evolution of the Antarctic Ice Sheet. Until recently, many of these datasets were unpublished in full, restricting further usage of the data for different applications such as gravity inversions, bed-reflectivity analysis, and englacial-layer tracking. Starting in 2020, scientists and data managers at the British Antarctic Survey have worked on standardising and releasing large swaths of aerogeophysical data acquired during the period 1994-2020, including a total of 64 datasets from 24 different surveys, amounting to ~450,000 line-km (or 5.3 million km²) of data across West Antarctica, East Antarctica, and the Antarctic Peninsula. Amongst these are the extensive surveys over the fast-changing Pine Island (BBAS 2004-05) and Thwaites (ITGC 2018-20) glacier catchments, and the first ever surveys of the Wilkes Subglacial Basin (WISE-ISODYN 2005-06) and Gamburtsev Subglacial Mountains (AGAP 2007-09). Considerable effort has been made to standardise these datasets to comply with the FAIR (Findable, Accessible, Interoperable and Re-Usable) data principles, as well as to create the Polar Airborne Geophysics Data Portal (https://www.bas.ac.uk/project/nagdp/), which serves as a user-friendly interface to interact with and download the newly published data. This paper reviews how these datasets were acquired and processed, presents the methods used to standardise them, and introduces the new data-portal infrastructure and interactive tutorials that were created to improve the accessibility of the data. Lastly, we exemplify future potential uses of the datasets by extracting information on the continuity of englacial layering from the fully processed radar data. We believe this newly released data will be a valuable asset to future geophysical and glaciological studies over Antarctica and will extend significantly the life cycle of the data. All datasets included in this data release are now fully accessible at: https://data.bas.ac.uk. [ABSTRACT FROM AUTHOR]

Surface meltwater drains on several Antarctic ice shelves, resulting in surface and sub-surface lakes that are potentially critical for the ice shelf collapse. Despite these phenomena, our understanding and assessment of the drainage and refreezing of these lakes is limited, mainly due to lack of field observations and to the limitations of optical satellite imagery during polar night and in cloudy conditions. This paper explores the potential of backscatter intensity and of interferometric coherence and phase from synthetic aperture radar (SAR) imagery as an alternative to assess the dynamics of meltwater lakes. In four case study regions over Amery and Roi Baudouin ice shelves, East Antarctica, we examine spatial and temporal variations in SAR backscatter intensity and interferometric (InSAR) coherence and phase over several lakes derived from Sentinel-1A/B C-band SAR imagery. Throughout the year, the lakes are observed in a completely frozen state, in a partially frozen state with a floating ice lid and as open-water lakes. Our analysis reveals that the meltwater lake delineation is challenging during the melting period when the contrast between melting snow and lakes is indistinguishable. Despite this finding, we show using a combination of backscatter and InSAR observations that lake dynamics can be effectively captured during other non-summertime months. Moreover, our findings highlight the utility of InSAR-based observations for discriminating between refrozen ice and sub-surface meltwater and indicate the potential for phase-based detection and monitoring of rapid meltwater drainage events. The potential of this technique to monitor these meltwater change events is, however, strongly determined by the satellite revisit interval and potential changes in scattering properties due to snowfall or melt events. [ABSTRACT FROM AUTHOR]

HydroGFD3 (Hydrological Global Forcing Data) is a data set of bias-adjusted reanalysis data for daily precipitation and minimum, mean, and maximum temperature. It is mainly intended for large-scale hydrological modelling but is also suitable for other impact modelling. The data set has an almost global land area coverage, excluding the Antarctic continent and small islands, at a horizontal resolution of 0.25 ∘ , i.e. about 25 km. It is available for the complete ERA5 reanalysis time period, currently 1979 until 5 d ago. This period will be extended back to 1950 once the back catalogue of ERA5 is available. The historical period is adjusted using global gridded observational data sets, and to acquire real-time data, a collection of several reference data sets is used. Consistency in time is attempted by relying on a background climatology and only making use of anomalies from the different data sets. Precipitation is adjusted for mean bias as well as the number of wet days in a month. The latter is relying on a calibrated statistical method with input only of the monthly precipitation anomaly such that no additional input data about the number of wet days are necessary. The daily mean temperature is adjusted toward the monthly mean of the observations and applied to 1 h time steps of the ERA5 reanalysis. Daily mean, minimum, and maximum temperature are then calculated. The performance of the HydroGFD3 data set is on par with other similar products, although there are significant differences in different parts of the globe, especially where observations are uncertain. Further, HydroGFD3 tends to have higher precipitation extremes, partly due to its higher spatial resolution. In this paper, we present the methodology, evaluation results, and how to access the data set at 10.5281/zenodo.3871707. [ABSTRACT FROM AUTHOR]

Assessing the impact of uncertainties in ice-sheet models is a major and challenging issue that needs to be faced by the ice-sheet community to provide more robust and reliable model- based projections of ice-sheet mass balance. In recent years, uncertainty quantification (UQ) has been increasingly used to characterize and explore uncertainty in ice-sheet models and improve the robustness of their projections. A typical UQ analysis involves first the (probabilistic) characterization of the sources of uncertainty followed by the propagation and sensitivity analysis of these sources of uncertainty. Previous studies concerned with UQ in ice-sheet models have generally focused on the last two steps but paid relatively little attention to the preliminary and critical step of the characterization of uncertainty. Sources of uncertainty in ice-sheet models, like uncertainties in ice-sheet geometry or surface mass balance, typically vary in space and potentially in time. For that reason, they are more adequately described as spatio(-temporal) random fields, which account naturally for spatial (and temporal) correlation. As a means of improving the characterization of the sources of uncertainties in ice-sheet models, we propose in this paper to represent them as Gaussian random fields with Matérn covariance function. The class of Matérn covariance functions provides a flexible model able to capture statistical dependence between locations with different degrees of spatial correlation or smoothness properties. Samples from a Gaussian random field with Matérn covariance function can be generated efficiently by solving a certain stochastic partial differential equation. Discretization of this stochastic partial differential equation by the finite element method results in a sparse approximation known as a Gaussian Markov random field. We solve this equation efficiently using the finite element method within the Ice-sheet and Sea-level System Model (ISSM). In addition, spatio-temporal samples can be generated by combining an autoregressive temporal model and the Matérn field. The implementation is tested on a set of synthetic experiments to verify that it captures well the desired spatial and temporal correlations. Finally, we demonstrate the interest of this sampling capability in an illustration concerned with assessing the impact of various sources of uncertainties on the Pine Island Glacier, West Antarctica. We find that both larger spatial and temporal correlations lengths will likely result in increased uncertainty in the projections. [ABSTRACT FROM AUTHOR]